Degree Granting Department
Mark E. Luther, Ph.D.
William S. Arnold, Ph.D.
Steven A. Murawski, Ph.D.
Accountability Measures, Annual Catch Limit, Commercial Catch Reports, Small-Scale Fisheries
The lack of timely reporting of commercial fisheries landings interferes with effective management of fisheries in United States Virgin Islands (USVI). Federal law requires that landings be limited to prevent annual catch limits (ACLs) from being exceeded. Previous attempts to predict total landings have used historic data from prior fishing seasons to predict future landings rather than leveraging available in-season data to provide a more real-time prediction of landings. This study presents an in-season model that predicts total landings using partial reports from the current fishing year. This estimate of total landings, including error bounds around that estimate, can then be compared to the ACL established for the species to estimate potential deviations from the allowable landings and adjust effort accordingly. The performance of the model was tested in a retrospective analysis on historical commercial landings data. Differences between predicted and observed fishing year landings by defined cut-off dates were used to identify reasonable deadlines for fishery managers to begin making reliable predictions on total annual landings. On average, predictions can be made with less than 9% error with at least four months of partial data, and with less than 5% error with at least seven months of partial data. This model's in-season predictions should be useful to managers to prevent ACL overages, and to guide fishers in their application of effort within and among components of the fishery, for example, to shift effort from one fishery management unit to another in response to excessive landings.
Scholar Commons Citation
Vara, Mary Janine, "Developing an In-season Predictor of Commercial Landings for Quota Monitoring in the U.S. Virgin Islands" (2014). USF Tampa Graduate Theses and Dissertations.